docstr: Load existing checkpoint. To load a specific checkpoint, please provide both `version`
  and `perfix`. If `version` is None, then the latest checkpoint under the specified
  directory will be loaded.
  
  :param path: Path to the existing checkpoint (or directory containing Orca checkpoint
         files).
  :param version: checkpoint version, which is the suffix of model.* file, i.e., for
         modle.4 file, the version is 4. If it is None, then load the latest checkpoint.
  :param prefix: optimMethod prefix, for example 'optimMethod-Sequentialf53bddcc'
  :return:
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code: def load_orca_checkpoint(self, path, version=None, prefix=None):
      """
      Load existing checkpoint. To load a specific checkpoint, please provide both `version`
      and `perfix`. If `version` is None, then the latest checkpoint under the specified
      directory will be loaded.
  
      :param path: Path to the existing checkpoint (or directory containing Orca checkpoint
             files).
      :param version: checkpoint version, which is the suffix of model.* file, i.e., for
             modle.4 file, the version is 4. If it is None, then load the latest checkpoint.
      :param prefix: optimMethod prefix, for example 'optimMethod-Sequentialf53bddcc'
      :return:
      """
      from bigdl.nn.layer import Model, Container
      from bigdl.optim.optimizer import OptimMethod
      from zoo.orca.learn.utils import find_latest_checkpoint
      import os
  
      if version is None:
          path, prefix, version = find_latest_checkpoint(
              path, model_type="bigdl"
          )
          if path is None:
              raise ValueError(
                  "Cannot find BigDL checkpoint, please check your checkpoint"
                  " path."
              )
      else:
          assert prefix is not None, (
              "You should provide optimMethod prefix, "
              "for example 'optimMethod-TorchModelf53bddcc'"
          )
  
      try:
          self.model = Model.load(
              os.path.join(path, "model.{}".format(version))
          )
          assert isinstance(
              self.model, Container
          ), "The loaded model should be a Container, please check your checkpoint type."
          self.optimizer = OptimMethod.load(
              os.path.join(path, "{}.{}".format(prefix, version))
          )
      except Exception:
          raise ValueError(
              "Cannot load BigDL checkpoint, please check your checkpoint path "
              "and checkpoint type."
          )
      self.estimator = SparkEstimator(
          self.model, self.optimizer, self.model_dir
      )
      self.nn_estimator = NNEstimator(
          self.model,
          self.loss,
          self.feature_preprocessing,
          self.label_preprocessing,
      )
      if self.optimizer is not None:
          self.nn_estimator.setOptimMethod(self.optimizer)
      self.nn_model = NNModel(
          self.model, feature_preprocessing=self.feature_preprocessing
      )
  
imports: from zoo.orca.learn.metrics import Metric
  from zoo.orca.learn.utils import bigdl_metric_results_to_dict
  from zoo.pipeline.nnframes import NNEstimator, NNModel
  from zoo.pipeline.estimator import Estimator
  from zoo.orca.learn.spark_estimator import Estimator
  from zoo.orca.data import SparkXShards
  from bigdl.optim.optimizer import MaxEpoch
  from zoo.feature.common import FeatureSet
  from zoo.orca.learn.metrics import Accuracy
  from pyspark.sql.dataframe import DataFrame
label_desc: Use image node_id 8 for calling the BigDLEstimator obj's underlying member method code with example usage: obj.load_orca_checkpoint(path, version, prefix) without return types